/tmp/ipykernel_6793/3287265490.py:1: DtypeWarning:
Columns (4,5,6,8) have mixed types. Specify dtype option on import or set low_memory=False.
ds
unique_id
y
2022-01-01 00:00:00
ZONA
1707.0
2022-01-01 01:00:00
ZONA
1673.0
2022-01-01 02:00:00
ZONA
1644.0
2022-01-01 03:00:00
ZONA
1605.0
2022-01-01 04:00:00
ZONA
1550.0
2022-01-01 05:00:00
ZONA
1487.0
2022-01-01 06:00:00
ZONA
1422.0
2022-01-01 07:00:00
ZONA
1373.0
2022-01-01 08:00:00
ZONA
1336.0
2022-01-01 09:00:00
ZONA
1317.0
fig = go.Figure()for i in ts["unique_id"].unique(): d =None d = ts[ts["unique_id"] == i] name = i, fig.add_trace(go.Scatter(x=d["ds"], y=d["y"], name = i, mode='lines'))fig.update_layout(title ="The Hourly Demand for Electricity in New York by Independent System Operator")fig
fig = plot_series(ts, max_ids=len(ts.unique_id.unique()), plot_random=False, max_insample_length=24*30,engine ="plotly")fig.update_layout(title ="The Hourly Demand for Electricity in New York by Independent System Operator")fig
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001655 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 266937, number of used features: 6
[LightGBM] [Info] Start training from score 1563.068468
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001579 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 268257, number of used features: 6
[LightGBM] [Info] Start training from score 1561.958936
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001630 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 267201, number of used features: 6
[LightGBM] [Info] Start training from score 1562.871599
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001664 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 268521, number of used features: 6
[LightGBM] [Info] Start training from score 1561.800124
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002297 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 267465, number of used features: 6
[LightGBM] [Info] Start training from score 1562.656823
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001542 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 268785, number of used features: 6
[LightGBM] [Info] Start training from score 1561.647109
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001749 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 267729, number of used features: 6
[LightGBM] [Info] Start training from score 1562.342567
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001886 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 269049, number of used features: 6
[LightGBM] [Info] Start training from score 1561.474617
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001641 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 267993, number of used features: 6
[LightGBM] [Info] Start training from score 1562.105268
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001553 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 269313, number of used features: 6
[LightGBM] [Info] Start training from score 1561.280340
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001539 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 268257, number of used features: 6
[LightGBM] [Info] Start training from score 1561.958936
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001578 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 269577, number of used features: 6
[LightGBM] [Info] Start training from score 1560.995920
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.006368 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 268521, number of used features: 6
[LightGBM] [Info] Start training from score 1561.800124
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001506 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 269841, number of used features: 6
[LightGBM] [Info] Start training from score 1560.692612
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001585 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 268785, number of used features: 6
[LightGBM] [Info] Start training from score 1561.647109
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001540 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 270105, number of used features: 6
[LightGBM] [Info] Start training from score 1560.509502
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001642 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 269049, number of used features: 6
[LightGBM] [Info] Start training from score 1561.474617
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002346 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 270369, number of used features: 6
[LightGBM] [Info] Start training from score 1560.363903
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001627 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 269313, number of used features: 6
[LightGBM] [Info] Start training from score 1561.280340
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001617 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 270633, number of used features: 6
[LightGBM] [Info] Start training from score 1560.230859
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001652 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 269577, number of used features: 6
[LightGBM] [Info] Start training from score 1560.995920
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001560 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 270897, number of used features: 6
[LightGBM] [Info] Start training from score 1560.064309
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001581 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 269841, number of used features: 6
[LightGBM] [Info] Start training from score 1560.692612
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001442 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 271161, number of used features: 6
[LightGBM] [Info] Start training from score 1559.877184
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001516 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 270105, number of used features: 6
[LightGBM] [Info] Start training from score 1560.509502
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001543 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 271425, number of used features: 6
[LightGBM] [Info] Start training from score 1559.603007
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002280 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 270369, number of used features: 6
[LightGBM] [Info] Start training from score 1560.363903
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001584 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 271689, number of used features: 6
[LightGBM] [Info] Start training from score 1559.341170
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001582 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 270633, number of used features: 6
[LightGBM] [Info] Start training from score 1560.230859
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001569 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 271953, number of used features: 6
[LightGBM] [Info] Start training from score 1559.180763
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001524 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 270897, number of used features: 6
[LightGBM] [Info] Start training from score 1560.064309
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002354 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 272217, number of used features: 6
[LightGBM] [Info] Start training from score 1559.068542
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001445 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 271161, number of used features: 6
[LightGBM] [Info] Start training from score 1559.877184
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001574 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 272481, number of used features: 6
[LightGBM] [Info] Start training from score 1558.914512
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001498 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 271425, number of used features: 6
[LightGBM] [Info] Start training from score 1559.603007
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001682 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 272745, number of used features: 6
[LightGBM] [Info] Start training from score 1558.781595
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001556 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 271689, number of used features: 6
[LightGBM] [Info] Start training from score 1559.341170
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001603 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 273009, number of used features: 6
[LightGBM] [Info] Start training from score 1558.672466
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002211 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 271953, number of used features: 6
[LightGBM] [Info] Start training from score 1559.180763
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004095 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 828
[LightGBM] [Info] Number of data points in the train set: 273273, number of used features: 6
[LightGBM] [Info] Start training from score 1558.428631
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:35: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:36: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:37: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:38: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:35: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:36: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:37: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:38: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:35: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:36: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:37: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:38: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003608 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 266937, number of used features: 28
[LightGBM] [Info] Start training from score 1563.068468
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003615 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 268257, number of used features: 28
[LightGBM] [Info] Start training from score 1561.958936
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004826 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 267201, number of used features: 28
[LightGBM] [Info] Start training from score 1562.871599
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004001 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 268521, number of used features: 28
[LightGBM] [Info] Start training from score 1561.800124
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003496 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 267465, number of used features: 28
[LightGBM] [Info] Start training from score 1562.656823
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005084 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 268785, number of used features: 28
[LightGBM] [Info] Start training from score 1561.647109
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004659 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 267729, number of used features: 28
[LightGBM] [Info] Start training from score 1562.342567
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003543 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 269049, number of used features: 28
[LightGBM] [Info] Start training from score 1561.474617
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003566 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 267993, number of used features: 28
[LightGBM] [Info] Start training from score 1562.105268
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004215 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 269313, number of used features: 28
[LightGBM] [Info] Start training from score 1561.280340
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003711 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 268257, number of used features: 28
[LightGBM] [Info] Start training from score 1561.958936
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003732 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 269577, number of used features: 28
[LightGBM] [Info] Start training from score 1560.995920
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003688 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 268521, number of used features: 28
[LightGBM] [Info] Start training from score 1561.800124
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003844 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 269841, number of used features: 28
[LightGBM] [Info] Start training from score 1560.692612
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.006156 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 268785, number of used features: 28
[LightGBM] [Info] Start training from score 1561.647109
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003580 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 270105, number of used features: 28
[LightGBM] [Info] Start training from score 1560.509502
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003674 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 269049, number of used features: 28
[LightGBM] [Info] Start training from score 1561.474617
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003675 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 270369, number of used features: 28
[LightGBM] [Info] Start training from score 1560.363903
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003692 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 269313, number of used features: 28
[LightGBM] [Info] Start training from score 1561.280340
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003725 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 270633, number of used features: 28
[LightGBM] [Info] Start training from score 1560.230859
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003944 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 269577, number of used features: 28
[LightGBM] [Info] Start training from score 1560.995920
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003777 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 270897, number of used features: 28
[LightGBM] [Info] Start training from score 1560.064309
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003546 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 269841, number of used features: 28
[LightGBM] [Info] Start training from score 1560.692612
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004576 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 271161, number of used features: 28
[LightGBM] [Info] Start training from score 1559.877184
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004111 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 270105, number of used features: 28
[LightGBM] [Info] Start training from score 1560.509502
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003788 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 271425, number of used features: 28
[LightGBM] [Info] Start training from score 1559.603007
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003904 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 270369, number of used features: 28
[LightGBM] [Info] Start training from score 1560.363903
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004916 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 271689, number of used features: 28
[LightGBM] [Info] Start training from score 1559.341170
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003741 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 270633, number of used features: 28
[LightGBM] [Info] Start training from score 1560.230859
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003791 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 271953, number of used features: 28
[LightGBM] [Info] Start training from score 1559.180763
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003665 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 270897, number of used features: 28
[LightGBM] [Info] Start training from score 1560.064309
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003650 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 272217, number of used features: 28
[LightGBM] [Info] Start training from score 1559.068542
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003539 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 271161, number of used features: 28
[LightGBM] [Info] Start training from score 1559.877184
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.007013 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 272481, number of used features: 28
[LightGBM] [Info] Start training from score 1558.914512
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003764 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 271425, number of used features: 28
[LightGBM] [Info] Start training from score 1559.603007
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.103024 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 272745, number of used features: 28
[LightGBM] [Info] Start training from score 1558.781595
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004331 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 271689, number of used features: 28
[LightGBM] [Info] Start training from score 1559.341170
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003825 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 273009, number of used features: 28
[LightGBM] [Info] Start training from score 1558.672466
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003796 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 271953, number of used features: 28
[LightGBM] [Info] Start training from score 1559.180763
[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003968 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 6438
[LightGBM] [Info] Number of data points in the train set: 273273, number of used features: 28
[LightGBM] [Info] Start training from score 1558.428631
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:35: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:36: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:37: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:38: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:35: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:36: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:37: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:38: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:35: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:36: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:37: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/workspaces/pydata-ny-ga-workshop/experimentation/backtesting.py:38: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy